---
title: Async Agent with Structured Output
---

This example demonstrates how to use async agents with structured output schemas, comparing structured output mode versus JSON mode for generating movie scripts with defined data models.

## Code

```python structured_output.py
import asyncio
from typing import List

from agno.agent import Agent, RunOutput  # noqa
from agno.models.openai import OpenAIChat
from pydantic import BaseModel, Field
from rich.pretty import pprint  # noqa


class MovieScript(BaseModel):
    setting: str = Field(
        ..., description="Provide a nice setting for a blockbuster movie."
    )
    ending: str = Field(
        ...,
        description="Ending of the movie. If not available, provide a happy ending.",
    )
    genre: str = Field(
        ...,
        description="Genre of the movie. If not available, select action, thriller or romantic comedy.",
    )
    name: str = Field(..., description="Give a name to this movie")
    characters: List[str] = Field(..., description="Name of characters for this movie.")
    storyline: str = Field(
        ..., description="3 sentence storyline for the movie. Make it exciting!"
    )


# Agent that uses structured outputs
structured_output_agent = Agent(
    model=OpenAIChat(id="gpt-5-mini-2024-08-06"),
    description="You write movie scripts.",
    output_schema=MovieScript,
)

# Agent that uses JSON mode
json_mode_agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    description="You write movie scripts.",
    output_schema=MovieScript,
    use_json_mode=True,
)


# Get the response in a variable
# json_mode_response: RunOutput = json_mode_agent.arun("New York")
# pprint(json_mode_response.content)
# structured_output_response: RunOutput = structured_output_agent.arun("New York")
# pprint(structured_output_response.content)

asyncio.run(structured_output_agent.aprint_response("New York"))
asyncio.run(json_mode_agent.aprint_response("New York"))
```

## Usage

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install libraries">
    ```bash
    pip install -U agno openai pydantic rich
    ```
  </Step>

  <Step title="Export your OpenAI API key">

    <CodeGroup>

    ```bash Mac/Linux
        export OPENAI_API_KEY="your_openai_api_key_here"
    ```

    ```bash Windows
        $Env:OPENAI_API_KEY="your_openai_api_key_here"
    ```
    </CodeGroup> 
   </Step>

  <Step title="Create a Python file">
    Create a Python file and add the above code.
    ```bash
    touch structured_output.py
    ```
  </Step>

  <Step title="Run Agent">
    <CodeGroup>
    ```bash Mac
    python structured_output.py
    ```
    
    ```bash Windows
    python structured_output.py
    ```
    </CodeGroup>
  </Step>

  <Step title="Find All Cookbooks">
    Explore all the available cookbooks in the Agno repository. Click the link below to view the code on GitHub:

    <Link href="https://github.com/agno-agi/agno/tree/main/cookbook/agents/async" target="_blank">
      Agno Cookbooks on GitHub
    </Link>
  </Step>
</Steps>